vfc.h

#ifndef _VECTOR_FIELD_CONSENSUS_H
#define _VECTOR_FIELD_CONSENSUS_H
// Mismatch removal by vector field consensus (VFC)
// Author: Ji Zhao
// Date:   01/25/2015
// Email:  zhaoji84@gmail.com
//
// Parameters:
//   gamma: Percentage of inliers in the samples. This is an inital value
//       for EM iteration, and it is not important. Default value is 0.9.
//   beta: Paramerter of Gaussian Kernel, k(x, y) = exp(-beta* || x - y || ^ 2).
//       Default value is 0.1.
//   lambda: Represents the trade - off between the goodness of data fit
//       and smoothness of the field.Default value is 3.
//   theta: If the posterior probability of a sample being an inlier is
//       larger than theta, then it will be regarded as an inlier.
//       Default value is 0.75.
//   a: Paramerter of the uniform distribution. We assume that the outliers
//       obey a uniform distribution 1 / a. Default Value is 10.
//   MaxIter : Maximum iterition times.Defualt value is 500.
//   ecr: The minimum limitation of the energy change rate in the iteration
//       process. Default value is 1e-5.
//   minP: The posterior probability Matrix P may be singular for matrix
//       inversion.We set the minimum value of P as minP. Default value is
//       1e-5.
//   method: Choose the method for outlier removal.There are three optional
//       methods : NORMAL_VFC, FAST_VFC, SPARSE_VFC. Default value is NORMAL_VFC.
//
//
// Reference
// [1] Jiayi Ma, Ji Zhao, Jinwen Tian, Alan Yuille, and Zhuowen Tu.
//     Robust Point Matching via Vector Field Consensus, 
//     IEEE Transactions on Image Processing, 23(4), pp. 1706-1721, 2014
// [2] Jiayi Ma, Ji Zhao, Jinwen Tian, Xiang Bai, and Zhuowen Tu.
//     Regularized Vector Field Learning with Sparse Approximation for Mismatch Removal, 
//     Pattern Recognition, 46(12), pp. 3519-3532, 2013


#include "opencv2/core/core.hpp"
#include <iomanip>
#include <iostream>

//#define PRINT_RESULTS
#define DOUBLE_PI (6.283185f)
#define inf (0x3f3f3f3f)
#define NORMAL_VFC 1
#define FAST_VFC   2
#define SPARSE_VFC 3
#define MIN_POINT_NUMBER 5
#define MIN_STANDARD_DEVIATION 0.1

using namespace cv;
using namespace std;

class VFC{
public:
	VFC();
	~VFC();
	bool setData(vector<Point2f> X1, vector<Point2f> X2);
	bool normalize();
	Mat constructIntraKernel(vector<Point2f> X);
	Mat constructInterKernel(vector<Point2f> X, vector<Point2f> Y);
	void initialize();
	void getP();
	void calculateTraceCKC();
	void calculateC();
	void calculateV();
	void calculateSigmaSquare();
	void calculateGamma();
	void optimize();
	void optimizeVFC();
	void optimizeFastVFC();
	void optimizeSparseVFC();
	vector<int> obtainCorrectMatch();
	// for FastVFC only
	void calculateTraceCQSQC();
	void calculateCFastVFC();
	// for SparseVFC only
	bool selectSubset();
	void calculateC_SparseVFC();
private:
	vector<int> _matchIdx;
	int _numPt; // number of matches
	int _numDim; // dimensions, fixed as 2 in current version
	int _numElement; // _numPt * _numElement
	vector<Point2f> _lX; // keypoint position in left image
	vector<Point2f> _rX; // keypoint position in right image
	vector<Point2f> _X; // start point of vector field
	vector<Point2f> _Y; // end point of vector field
	Mat _K; // kernel matrix
	vector<Point2f> _V; // vector field
	vector<Point2f> _C; // coefficient
	vector<float> _P; // probability
	float _sumP; // sum of _P
	float _sigma2; // deviation of Gaussian noise
	float _E; // energy
	float _traceCKC;
	
	// parameters
	int _method;
	int _maxIter;
	float _gamma;
	float _beta;
	float _lambda;
	float _theta;
	float _a;
	float _ecr;
	float _minP;

	// parameters for FastVFC
	int _numEig;
	Mat _S; // eigenvalues
	Mat _Q; // eigenvectors
	float _traceCQSQC;

	// parameters for SparseVFC
	int _numCtrlPts;
	vector<Point2f> _ctrlPts;
	Mat _U;

	// intermediate variables
	Point2f _meanLeftX;
	Point2f _meanRightX;
	float _scaleLeftX;
	float _scaleRightX;
};

#endif